Transactions on Information and Communications Technologies vol 9, 1995 WIT Press, ISSN

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1 A parallel approach to the extraction of boundary and shading information from digital images E. Ardizzone", A. Genco**, C. Lodato\ G. Lo Re\ R. Pirrone" "Dipartimento di Ingegneria Elettrica, Universita di Palermo, ^Centra Studi sulle Reti di Elaboratori, Consiglio Nazionale delle Ricerche, Viale delle Scienze, Palermo, Italy Abstract The Boundary Contour System (BCS) is a part of a computational model that is aimed to address the main issues in early vision processes, like boundary completion, grouping, brightness effects and so on. In this work we present a parallel implementation of the BCS carried out by means of a workstation network under the control of the PVM system. The obtained results are compared with those obtained in a previous sequential implementation of the model. The efficency of the distributed implementation is also discussed. 1 Introduction In this paper we propose a parallel implementation of an algorithm for the extraction of boundary and shading information from digital images, based on the Boundary Contour System (BCS). The BCS is a part of a larger computational model conceived by Grossberg and Mingolla [1][2] that is aimed to address the main issues in early vision processes, like boundary completion, grouping, brightness effects and so on. The model is based on a multi-layer architecture, with several stages cascaded in a loop way. The final result is a boundary detection and completion of regions present in the input scene and a computation of their brightness characteristics. We already built a non-parallel implementation of the BCS E.Ardizzone[3] and we also derived a set of suitable values for the parameters in the model equations. Since we are planning to apply the BCS model to real time applications with dynamic image acquisition, we designed a parallel implementation that will be capable of achieving the required performance.

2 316 High-Performance Computing in Engineering 2 Background: The Boundary Contour System The Boundary Contour System (BCS) is a part of a larger computational model conceived by Steven Grossberg and Ennio Mingolla that is aimed to address the main issues in early vision processes, like boundary completion, grouping, brightness effects and so on. This model is made up essentially by two neural architectures: the BCS and the PCS (Feature Contour System); the BCS is mainly responsible for boundary detection and completion in the input scene while the PCS computes the brightness characteristics of the perceived scene and fills the regions detected by the BCS with the correct colour. Both the BCS and the PCS take their input from a monocular preprocessing stage that acts as a retina. The retinal stage performs a centersurround filtering that is used for two purposes; it acts as a gain control over the true input signal and enhances image contrasts, while preserving contrasts ratios at the edges and suppressing uniform regions and noise. In this way the so called illuminant discounting is performed that is the computation of contrast ratios regardless of the illumination conditions. In this work we are concerned only in the BCS: the rest of this section is devoted to the description of the BCS structure. We have built a computer implementation of the BCS and we also derived a set of suitable values for the parameters in the model equations. In our work we followed the model description that is present in [1] and [2]. We first describe the preprocessing stage of the model that represents the true input to the BCS. The BCS/FCS theory defines this stage as a two channels filter with an ON channel and an OFF channel. The ON channel performs an on center-off surround filtering. Conversely the OFF channel performs an off center-on surround filtering. In our simulations we followed the work of Grossberg and his colleagues who didn't use the OFF channel so in our model the retinal stage acts only as a ON filter. The activation of this stage is obtained solving at equilibrium a classical shunting equation of the form: -=-Dx + (U-xCI-(x + LSI (1) where D is the decay rate of the activation while U and -L represent the bounding values of the range of variation of Xy. Ipq is the image intensity and Cpqij and Spqij are two bi-dimensional gaussian kernels centered in (ij). The solution of (lj has a bilinear form with a DoG in the numerator and a SoG in the denominator. The Boundary Comtour System has the structure of a multilayer feedback network made up by two main blocks: a feedforward block called OC Filter and the real feedback stage called CC Loop (see figure 1). All the neuron layers in the net are arranged as three-dimensional arrays of cells: the first two dimensions are for the spatial displacement of the cells over the input image while the third one indicates the orientation to which a single cell is sensible. In this way clusters of cells are regularly displaced all over the image plane and inside of each cluster the single cells span all possible orientations. Each cluster processes the output of the cluster in the same spatial position of the preceding layer. In our computer simulations we used N cells for each cluster with N= 1 2.

3 High-Performance Computing in Engineering 317 The Oriented Contrast (OC) Filter takes its input from the retinal stage and feeds it to N pairs of cells with elongated receptive fields that are sensible to the amount of contrast across their principal symmetry axis. These cells are called Simple Cells. The two cells of a pair are sensible to opposite directions of contrast along the same orientation; the output is computed summing the activations of each couple of cells. The Simple Cells feed their activation to the Complex Cells that have the same elongated receptive fields but are insensitive to the direction of contrast. Their activation is the output of the whole OC Filter. The BCS theory uses the OC Filter to roughly determine the orientation and position of the image contrasts, while the Cooperative-Competitive (CC) Loop is aimed to the detection, sharpening and completion of the image boundaries. The first stage of the CC Loop takes its input from the Complex Cells and performs a short-range competition between nearby cells with the same orientation: a cell in this layer sends inhibitory signals to the cells in its spatial neighborood that have the same orientation. ORIENTED COOPERATION ^ o o o o o o o o. o o o o 1. «. -h- \z> ^^ ^ ( 1 4 N 0 O o o (y) 00 o o (w) o o o o o o o o CC:LOOP OC"FILTER Fig. 1: The BCS structure The second stage is again a competitive one. In this case competition is a push-pull opponent process: couples of cells in the same position but with perpendicular orientation send inhibitory signals to each other. When an orientation is excited the perpendicular orientation is inhibited while when an orientation is inhibited the perpendicular one is excited via disinhibition. The two competitive stages have the task to eliminate the positional uncertainty of the edges. This positional uncertainty derives from the measurement errors that are present in the detection of the image contrasts by the OC Filter; an edge can fall everywhere within the receptive field of a Simple Cell and this gives rise to the uncertainty.

4 318 High-Performance Computing in Engineering The positional disambiguation is achieved in two steps. If we consider an edge in the image, first the competition 1 inhibits the weak activations of cells that are placed beyond the effective end of the edge, but partially overlap it with their receptive field. Second, the competition 2 enforces via disinhibition the activation near the end of the edge that are perpendicular to the edge itself. Such activations are called endcuts. The competitive processes give rise to boundary fragments that are not very sharp and can support completion within a band of orientations. To obtain correct completion a cooperative stage is employed. The cells in this stage are called Bipole Cells; they have very elongated receptive fields with two lobes along the orientation to which the cell is sensitive. A Bipole Cell takes the activations from the competitive cells in the neighborood of its position that are sensible to its orientation; it fires only if both sides of its receptive field receive a sufficient amount of activation. If there is an emergent contour along one orientation near the position of a Bipole Cell that is sensible to this orientation, then the cell fires and via the feedback path it enforces the activation of the competitive cell, in the first stage of the CC Loop, placed in the same position and sensitive to the same orientation. Along the feedback path, there is the last stage of the CC Loop that is the so called Feedback Competitive Stage. The kind of competition in this stage is quite the same as in the competition 1 stage; this stage has been introduced to disambiguate the boundary completion uncertainty; competition 2 generates in the same position different boundary fragments that are completed by the cooperative stage with variable intensity depending on the strength of their activation. A competitive mechanism is therefore necessary to ensure that only the strongest complete boundary can survive and the corresponding activation can be added to the competition 1 cell activity. Boundary completion requires some time to became stable due to its loop nature; the result of this process is an image segmentation in which several bounded regions are present. These regions are taken as input from the PCS that fills each region with a brightness value that is computed as a feature of the corresponding region in the input image. 3 The Parallel implementation The first step in trying problem decomposition was to evaluate the relative working load of each section of our BCS model implementation. As it can be seen in the flow chart of Fig. 2, the most relevant part is played by the cooperation module. It covers about the 80 % of the whole working load of the main loop, and therefore, we decided to investigate the parallelism of this part only. Furthermore, the intrinsic logic of this module appeared to be easily decomposable. The different orientations of contrast can be processed separately by means of co-operating processes.

5 High-Performance Computing in Engineering 319 FILE_LOADINq^ I SCREEN, _,. UNITIALIZE DEF_MASK ~~l COMPETITION 1st [COMPETITION 2nd CALC_KERNELI 80% 1 J_ I COOPERATION 10% [FEEDBACK I Fig. 2: The block diagram of the BCS algorithm 3.1 The distributed design We carried out the parallel implementation trying to distribute the working load among the available workstations connected into our Ethernet LAN. To this end we decided to exploit the features of the PVM (Parallel Virtual Machine) [4] environment. As it is well known, PVM is a free software that was developed by a group of researchers leaded by J. Dongarra at the Oak Ridge National Laboratory (Tennessee). It allows a master task to create new tasks to be run in selected workstations of the network. It also provides both synchronous and asynchronous communication primitives, synchronisation barriers, and many others language extensions to standard C, C++, and Fortran, to manage the distributed environment. We considered some different approaches to the parallel BCS implementation. One is based on the fact that this model is used for image recognition problems, where the object or the observer assume different positions. Under this hypothesis, a time parallelism schema could be considered between the image acquisition period, the initialisation phase and the elaboration code. Another option consists in partitioning the image matrix that sometimes can be a very large one. The last approach considers the different orientations of contrast of each point. They can be elaborated separately by different processes that are running in parallel. We chose this last strategy because it is the one that can be performed in any case: when the matrix is small or large, and when no dynamic acquisition has to be considered. However, this partial form of parallelism leaves a relevant part of sequential code. This affects the efficiency of the parallel solution that could be improved when the size of the problem and the application makes the first two approaches applicable. 3.2 Implementation notes When developing the distributed application in the PVM environment, we had to tackle the typical overhead factors of a distributed system. In particular the communication medium played a determinant role in time performances

6 320 High-Performance Computing in Engineering because the ethernet Ian is a shared-sequential medium and never allows two different communications to perform in parallel: their total completion time always results to be the sum of the two periods at least. An other aspect was the different data representation formats inside the different machines. This entails a conversion overhead if communication takes place between machines with different architectures. To this end, PVM adopts a common format, that is the XDR (external Data Representation) protocol by SUN Microsystems [5]. Our implementation follows the typical application structure of the PVM environment that is the master-slave schema. The master task (each process in the PVM terminology is a task) "spawns" the other tasks (slaves) allocating them in selected machines. The master task executes all the BCS modules sequentially, except for the co-operation module. For this step it creates a number of slaves equal to the number of the used machines. Then it assigns the calculations of the different orientations of contrast to each one, according to a suitable load distribution. In addition, the master task broadcasts the initial data, and collects the intermediate results of the co-operation at each iteration. 4 Experiments and results Our distributed environment consists of six workstations that are heterogeneous in terms of architecture and performance. Their hardware features are reported in Tab. 1. Tab. 1: hardware features of the workstations used workstation type processor RISC27 IBM RISC POWER 62 MHz CEREUX DEC 3000 ALPHA 175 MHz POWER RISC System/ POWER PC 66 MHz ALPHA DEC 3000 ALPHA 125 MHz SUNIPA SUN SPARC stat. 10 SPARC Supsc 50 MHz CUCAIX RISC System/ POWER 20 MHz mem. 64Mb 32Mb 32Mb 48%% 64Mb 32Mb In order to achieve an efficient load distribution, we firstly ran a benchmark that consisted in executing the same code on the different workstations. In particular, we used the co-operation module, once in its original sequential version and once in the multi-process version. This last test allowed us to evaluate a performance-index for each machine. These indexes were used to state the initial load distribution among the tasks running in the used workstations. Some adjustments were done considering other important aspects that affects the parallel performance, such as the communication medium and the different data representation adopted by the involved machines. Tab. 2 reports the execution times of the sequential code and Tab.3 the results of the benchmark in terms of the multi-process execution time and performance index of each workstation.

7 High-Performance Computing in Engineering 321 Tab. 2: sequential execution workstation RISC27 CEREUX POWER ALPHA SUNIPA CUCAIX cooperation Tab. 3: multi-process execution on a single workstation workstation cooperation peformance index RISC CEREUX POWER ALPHA SUNIPA CUCAIX As far as the efficiency evaluation is concerned, we should consider that the above workstations are heterogeneus, and therefore, we cannot correctly evaluate it as the ratio between the relative speedup and the number of used processor. Instead of this last quantity, we adopted a term that was evaluated as the ratio between the sum of the performance indexes of the used machines and the one of the workstation hosting the master task. Tab. 4: most representative results obtained allocating the master task in each workstation. master slaves 3 - CEREUX cooperation speedup efficiency RISC SUNIPA 5 - RISC RISC27 CEREUX 2 - ALPHA 4 - CEREUX 4 - CEREUX POWER 2 - SUNIPA 6 - RISC SUNIPA ALPHA 6-RISC CEREUX 3 - CEREUX 2 - POWER SUNIPA 2 - SUNIPA 5 - RISC POWER CUCAIX 4 - SUNIPA 5 - RISC As it can be observed, not all the configurations envolve all the workstations. This is due to the fact that, according to the performance indexes, the slowest machines should host a number of processes to low. In some cases this is not an effective option, and, generally speaking, it is better to restrict the configuration within a set of workstations not excessively different, especially when the master task runs on the fastest machine. It is relevant to observe that, while the efficiency value is roughly constant for all the configurations, the best speedup is relative to the configuration were the master task runs in the slowest machine. In this case the multi-process solution is four times faster than the sequential one.

8 322 High-Performance Computing in Engineering 5 Conclusions A parallel version of the BCS algorithm has been proposed. In particular an implementation of the cooperation module has been carried out distributing the computation of the possible orientations of the brightness gradient on different co-operating processes. A workstation network has been employed as a distributed system for the execution of the parallel experiments. The must relevant results have been reported that were obtained by trials with different configurations of the distributed system. The speed-up values rang from 1.58 to 4 in dependence of the processing rate of the involved machines. The paralellism degree has been bounded to twelve orientations, because these appeared to be sufficient for a good approximation of the actual direction of the brightness gradient. Nevertheless the model allows a larger number of directions to be evaluated. In this case the parallel implementation turns out to be more effective in reducing the processing time. References 1. Grossberg, S. & Mingolla, E. Neural dynamics of perceptual grouping: Textures, boundaries, and emergent segmentations, Perception & j,1985,38(2), Grossberg, S. & Mingolla, E. Neural dynamics of form perception: Boundary webs, illuminants, and shape-from-shading, Computer Vision, Graphics and Image Processing, 1987,37, E. Ardizzone, A. Chella, R. Pirrone, F. Sorbello: Recovering 3-D Form Features by a Connectionist Architecture, Pattern Recognition Letters, 15, (1994), G.A. Geist & V.S. Sunderam, Network-Based Concurrent Computing on the PVM System, Concurrency: Practice and Experience, 19924(4). 5. SunSoft, SunOS 5.3 Network Interfaces Programmer's Guide, XDR Protocol Specification, 1993 part number ,.261 -